Purpose :
As data warehouses with data from electronic health records (EHR) provide benefits for clinical research, adding information from imaging devices (e.g. retinal thickness from OCT) is useful. To overcome limitations in bulk exporting OCT measurements in device software, we implemented a bot script, which emulates mouse and keyboard strokes, to gather this data. To check accuracy of automatic OCT segmentation in our sample, we compared the measurements with manually segmented and exported OCT scans.

Methods :
Since OCT devices often not come with a batch export function of aggregated information, a robotic script was implemented in Java utilizing the java.awt.Robot package running on 2 desktop PCs at the same time. The script used image recognition techniques (based on pixel colour values) to identify specific types of OCT data and consequently adapt its behavior and start a type-specific export process based on a sequence of mouse clicks and keyboard strokes. Exported XML files were added to our smart eye database using an individual developed Java based routine. Manual segmentation and export of 7077 OCT (Spectralis OCT, Heidelberg Engineering, Germany) volume scans of patients under active Anti-VEGF treatment was performed.

Results :
72.894 OCT scans were exported and stored in the data warehouse. The initial export process took about 60 full days. The ETDRS grid within the software was changed in 11% of cases from its default position by 7.5 pixel (SD +/-53.1pixel) in automatically segmented scans vs. in 82.9% of manually segmented cases by 32.5 pixel (63.9 pixel). The average Central retinal thickness (CRT) was 344.8 um (+/- 173.6) (manually segmented) vs. 341.2 um (+/- 138.4) (automatic segmented). Figure 1 shows a Bland-Altman plot of OCT scans of 600um or less in the uncorrected export.

Conclusions :
Batch exporting OCT scans using a bot script is a possible way, if not provided in the device’s software. We achieved export in a relatively short time compared to manually triggered export. In a certain range of uncorrected CRT (200-400 um), most automatic segmentation done was precise. New technologies like deep learning may overcome the issue of malsegmentation and provide better results in thickened retinas.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.